88 citations · 88 across the 12 of their papers we have counts for
12 papers
Retrieval-Augmented Generation with Covariate Time Series
Kenny Ye Liang, Zhongyi Pei, Huan Zhang +3
While RAG has greatly enhanced LLMs, extending this paradigm to Time-Series Foundation Models (TSFMs) remains a challenge. This is exemplified in the Predictive Maintenance of the…
Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
Jiafeng Lin, Mengren Zheng, Simeng Ye +5
Time series forecasting has witnessed an increasing demand across diverse industrial applications, where accurate predictions are pivotal for informed decision-making. Beyond numer…
Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting
Yunzhong Qiu, Binzhu Li, Hao Wei +5
Electricity market prices exhibit extreme volatility, nonlinearity, and non-stationarity, making accurate forecasting a significant challenge. While cutting-edge time series founda…
Boosting AI Reliability with an FSM-Driven Streaming Inference Pipeline: An Industrial Case
Yutian Zhang, Zhongyi Pei, Yi Mao +3
The widespread adoption of AI in industry is often hampered by its limited robustness when faced with scenarios absent from training data, leading to prediction bias and vulnerabil…
Thoth: Mid-Training Bridges LLMs to Time Series Understanding
Jiafeng Lin, Yuxuan Wang, Jialong Wu +3
Large Language Models (LLMs) have demonstrated remarkable success in general-purpose reasoning. However, they still struggle to understand and reason about time series data, which…
Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models
Yunzhong Qiu, Zhiyao Cen, Zhongyi Pei +2
Large time series models (LTMs) have emerged as powerful tools for universal forecasting, yet they often struggle with the inherent diversity and nonstationarity of real-world time…
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
Zhongyi Pei, Zhiyao Cen, Yipeng Huang +4
Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-b…
DualWeaver: Synergistic Feature Weaving Surrogates for Multivariate Forecasting with Univariate Time Series Foundation Models
Jinpeng Li, Zhongyi Pei, Huaze Xue +3
Time-series foundation models (TSFMs) have achieved strong univariate forecasting through large-scale pre-training, yet effectively extending this success to multivariate forecasti…
TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts
Jiafeng Lin, Yuxuan Wang, Huakun Luo +2
Multimodal time series forecasting has garnered significant attention for its potential to provide more accurate predictions than traditional single-modality models by leveraging r…
TimesBERT: A BERT-Style Foundation Model for Time Series Understanding
Haoran Zhang, Yong Liu, Yunzhong Qiu +4
Time series analysis is crucial in diverse scenarios. Beyond forecasting, considerable real-world tasks are categorized into classification, imputation, and anomaly detection, unde…
Multi-Adversarial Domain Adaptation
Zhongyi Pei, Zhangjie Cao, Mingsheng Long +1
Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy be…
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
Chi Lin, Guowei Wu, Feng Xia +3
In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and opera…